模拟大气湍流与指向误差，提升遥感图像检测模型训练真实性。
Impact of Atmospheric Turbulence and Pointing Error on Earth Observation
- 构建物理级图像模拟器，融合垂直路径湍流与卫星指向抖动。
- 弱湍流下YOLOv8召回率从91%降至60%，强干扰时低于40%。
- RetinaNet更鲁棒，复杂环境下召回率稳定在75%左右，适合实际应用。
地球观测（EO）图像常受大气湍流和指向抖动影响而退化，但现有用于训练基于AI的目标检测模型的数据集极少考虑这些因素。本文基于前期工作，提出一种增强型图像模拟器，可引入垂直路径大气湍流及由平台与传感器振动引起的卫星指向抖动，生成具有物理真实性的退化图像。以船舶检测为例，在不同湍流与指向误差水平下，使用YOLOv8和RetinaNet进行评估。结果显示，在理想条件下YOLOv8召回率为91%，弱湍流下降至60%，强湍流或抖动时低于40%；而RetinaNet表现出更强鲁棒性，退化条件下召回率维持在约75%。研究强调，将真实物理退化纳入地球观测训练数据集，对确保AI模型在实际运行环境中的可靠性能至关重要，尤其在海上监视等场景中。
原文摘要 · Abstract (English)
Earth Observation (EO) imagery is often degraded by atmospheric turbulence and pointing jitter; yet, these effects are rarely considered in datasets used to train AI-based detection models. Based on prior work, this paper presents an enhanced image simulator that enables the incorporation of vertical-path atmospheric turbulence and satellite pointing jitter, arising from platform and sensor vibrations, to generate physically realistic distorted images. As a case study, vessel detection is evaluated using YOLOv8 and RetinaNet on images generated by the proposed simulator under different levels of turbulence and pointing errors. Results show that YOLOv8 recall decreases from 91% under ideal conditions to 60% in the presence of weak turbulence, and falls below 40% under strong turbulence or jitter. In contrast, RetinaNet demonstrates greater robustness, maintaining approximately 75% recall across degraded conditions. These results highlight the importance of incorporating realistic physical degradations into EO training datasets to ensure reliable performance of AI-based models in operational environments, as demonstrated in maritime surveillance applications.
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